Mars (Liyao) Gao

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Ph.D. student
Paul G. Allen School of Computer Science & Engineering
University of Washington

About me

I am a Ph.D. student in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, advised by Professor J. Nathan Kutz. My research lies in AI for scientific discovery, with a focus on developing interpretable and generalizable learning frameworks for complex spatiotemporal and dynamical systems. I work at the intersection of symbolic regression, sparse modeling, and deep learning, aiming to uncover governing equations and enable reliable long-term prediction to accelerate scientific understanding. My long-term goal is to build robust machine learning methods that can bridge data and physical laws across domains like physics, climate science, fluid dynamics, neuroscience, and materials science. I am broadly interested in deep learning, statistical learning theory, Bayesian methods, time-series modeling, scientific computing, and recently agentic flow for science.

News

[June 2025] Transformer-SHRED paper led by amazing Alexey Yermakov now out on arXiv!! [arXiv] New
[May. 2025] Mesh-free SINDy paper collab and advised by amazing Bernat Font now out on arXiv!! [PDF] New
[Apr. 2025] Invited talk @ UCSB Applied Math seminar, UW CS4Env, and MIT in Marin Soljačić’s group.
[Mar. 2025] Our newest work "Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks" with isotropic flow and convex loss landscape visualization is now available on arXiv! [Website] [Colab] [Github] New
[Oct. 2024] Invited talk @ Georgia Tech ACMS seminar.
[Mar. 2024] Our paper "Bayesian autoencoders for data-driven discovery of coordinates, governing equations, and fundamental constants," is now published in PRSA!

Selected publications

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Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants.
Mars L. Gao, J. Nathan Kutz.
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

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Convergence of uncertainty estimates in ensemble and Bayesian sparse model discovery.
Mars L. Gao, Urban Fasel, Steven L. Brunton, J. Nathan Kutz.
In submission.

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Deformation Robust Roto-Scale-Translation Equivariant CNNs.
Mars L. Gao, Wei Zhu, Guang Lin.
Transaction of Machine Learning Research.

Contact

Email: marsgao [at] uw [dot] edu